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Zettersten, Martin; Schonberg, Christina; Lupyan, Gary – First Language, 2020
This article reviews two aspects of human learning: (1) people draw inferences that appear to rely on hierarchical conceptual representations; (2) some categories are much easier to learn than others given the same number of exemplars, and some categories remain difficult despite extensive training. Both of these results are difficult to reconcile…
Descriptors: Models, Language Acquisition, Prediction, Language Processing
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MacKenzie D. Sidwell; Landon W. Bonner; Kayla Bates-Brantley; Shengtian Wu – Intervention in School and Clinic, 2024
Oral reading fluency probes are essential for reading assessment, intervention, and progress monitoring. Due to the limited options for choosing oral reading fluency probes, it is important to utilize all available resources such as generative artificial intelligence (AI) like ChatGPT to create oral reading fluency probes. The purpose of this…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Oral Reading
Chafe, Wallace – 1990
Ease of language processing varies with the nature of the language involved. Ordinary spoken language is the easiest kind to produce and understand, while writing is a relatively new development. On thoughtful inspection, the readability of writing has shown itself to be a complex topic requiring insights from many academic disciplines and…
Descriptors: Comparative Analysis, Difficulty Level, Higher Education, Language Processing
Flaherty, Etienne – 1979
Controlling the rate of oral delivery in the second language classroom has been demonstrated as beneficial to the learner, whose information-processing capacity is an important factor in listening comprehension. Rate of information delivery can be controlled by limiting the actual rate of words per unit of time, or by maintaining content…
Descriptors: Cognitive Processes, Difficulty Level, Educational Testing, Language Instruction
Evens, Martha; And Others – 1986
Advanced learners of second languages and natural language processing systems both demand much more detailed lexical information than conventional dictionaries provide. Text composition, whether by humans or machines, requires a thorough understanding of relationships between words, such as selectional restrictions, case patterns, factives, and…
Descriptors: Adjectives, Computational Linguistics, Dictionaries, Difficulty Level